Transfer Learning: Techniques and Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 November 2025 | Viewed by 93
Special Issue Editors
Interests: machine learning and deep learning; industrial artificial intelligence; transfer learning and lifelong learning; deep reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Transfer learning has become essential in advancing artificial intelligence and deep learning, enabling the effective reuse of knowledge across domains, tasks, and data distributions. It plays a critical role in overcoming challenges such as data scarcity, domain shift, and generalization to unseen environments, making it vital in AI applications.
This Special Issue aims to compile recent advances in transfer learning, including novel methods, theoretical foundations, and practical applications. Topics of interest include, but are not limited to, the following:
- Novel methods and theoretical frameworks for transfer learning and domain adaptation.
- Novel pre-training strategies (e.g., self-supervised, supervised, domain-specific) and their impact on downstream tasks.
- Novel methods for cross-domain, cross-task and cross-modal transfer learning.
- Techniques for dealing with domain shift, catastrophic forgetting, and negative transfer.
- Transfer learning across different data modalities (e.g., vision, text, speech, graphs, time series) or structures.
- Applications of transfer learning to solve real-world challenges (e.g., simulation to reality, medical image analysis, robotics, autonomous systems, computer vision, engineering and Industry 4.0).
- Empirical investigations and benchmarks comparing different transfer learning approaches.
- Investigations into the robustness, fairness, and limitations of transfer learning methods.
- Efficient adaptation methods for large foundation models and LLMs.
We hope to provide a collection of high-quality papers that reflect the current state of the art and emerging research directions in transfer learning.
We look forward to receiving your contributions.
Dr. Hasan Tercan
Dr. Yang Li
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- transfer learning
- domain adaptation
- deep learning
- artificial intelligence
- pre-training
- knowledge transfer
- machine learning
- sim-to-real transfer
- transfer learning in continual and dynamic environment
- test-time adaptation
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
